1. Technical Field
This disclosure relates to finger print recognition, and more particularly, to a system and method for evaluating for fingerprint systems, which is capable of predicting performance with a high level of confidence without the need to acquire a large number of testing images.
2. Description of the Related Art
Fingerprint may be performed electronically to verify a person""s identity for different applications, for example, entry into a secured area, access to a bank account, etc. Referring to FIG. 1, a fingerprint verification system 10 typically includes the following components. A sensor 12 is included that acquires fingerprint images, often at a resolution of about 500 pixels per inch (ppi). A feature extraction module 16 converts image pixels into a small set of characteristic features for concise image representation. The most commonly used features are the minutiae of the fingerprint; i.e. the ridge ending or bifurcation points. Other features include cores and deltas, ridge count between minutia pairs, and ridge width. Module 16 may also include a quality control sub-module 14, which can provide feedback to users on poorly acquired images or non-fingerprint images. A fingerprint matcher 18 assigns a similarity score between a search (candidate) print and a reference print from a database 20, and decides whether to declare a match between the pair. Normally the matcher relies entirely on the features provided by the feature extractor 16.
In block 30, components are provided for training system 10 to associate a given person to their fingerprint. Fingerprint data is stored in database 20 and employed by matcher 18 to later verify users stored in database 20. Matcher 18 is part of an authentication module 32.
In FIG. 1, the verification algorithm of the system 10 is divided into two modules: feature extractor 16 and matcher 18. This separation is important in the context of performance evaluation. It permits analysis of matcher performance in isolation from the more sensor-dependent feature extractor. The matcher is nearly sensor independent. In spite of their intimate inter-relationship, these two modules tend to be impacted differently by factors affecting the performance of a fingerprint system. For instance, image warping due to finger elasticity affects exclusively the matcher 18, while intensity shift caused by moisture in fingers affects mostly the feature extractor 16. Of course, errors made by the feature extractor 16 are propagated through the matcher 18 and their effects need to be analyzed as well.
Since fingerprint systems are subject to strong statistic errors and the complex biometric features used for matching generally cannot be accurately described by mathematical models, it is very important to expose the systems to rigorous tests to assess their performance during development. A common method for system evaluation and validation is to use large-scale field tests. While this approach is effective, it is very costly and time consuming. Alternatively, existing fingerprint databases may be used to evaluate the algorithm portion of the system, e.g., the feature extractor 16 and the matcher 18; however, the testing results are often skewed due to at least the reasons listed below:
1. The characteristics of the sensor from which the database was constructed are often different from the sensor in the system under evaluation. As a result, the performance of the feature extractor, usually tuned to a particular sensor for optimal performance, cannot be subjectively and realistically assessed.
2. Even if the feature extractor were tuned for images in the database, the performance of the matcher is still biased by characteristics of the statistical variability and distortion of images in the database. Performance degradation due to poorly extracted features often cannot be easily separated from inherent deficiencies of the matcher.
Another option for system performance evaluation is to use synthetic fingerprint images, for example, synthetic fingerprint images generated by OPTEL, LTD. software, e.g., Fingerprint Synthesis(trademark). However, there exist severe limitations in the usefulness of synthetic fingerprint images. The most severe and also hardest to overcome is the extreme difficulty of generating synthetic images that can realistically mimic the characteristics of defects, artifacts and noise naturally present in real fingerprint images. As a result, the performance of a feature extractor in synthetic images tends to be a poor predictor for its performance in real images. In addition, the natural distribution of finger features is very complex and cannot be easily characterized by simple statistical models. For instance, random distribution of minutiae tends to produce optimistic estimates of FAR (false acceptance rate) and FRR (false rejection rate) distributions.
Therefore, a need exists for an evaluation technology for fingerprint systems, which is capable of predicting system performance with high confidence without the need to acquire a large number of testing images. A further need exists for a method for evaluating fingerprint systems which is accurate, quick and economical.
A system and method for evaluating a biometric detection system, in accordance with the present invention, provides an edited database including a plurality of existing biometric images with corrected extracted features that were acquired with a sensor or sensors different from the sensor of the system under evaluation. A second database, smaller than the edited database, is edited which includes biometric images (that were acquired by the sensor of the system under evaluation) are employed to evaluate the biometric detection system. The second database has errors in extracted features corrected. A statistical perturbation model is constructed to describe degradation characteristics of the extracted features from the second database as provided in the editing step. The statistical perturbation model is applied to the edited database to construct a perturbed database sensitive to degradations of the biometric system under evaluation. The biometric system is evaluated against the edited database and the perturbed database to predict a performance of the biometric system.
Another method, in accordance with the present invention, for evaluating a biometric detection system, includes the steps of providing a first database having a plurality of biometric images representative of a predetermined population, editing the first database to construct an edited database, the edited database having errors in extracted features from the first database corrected, providing a second database, smaller than the first database, which includes biometric images employed to evaluate the biometric detection system, the second database being representative of a sample of the predetermined population, editing the second database to construct an edited database having errors in extracted features from the second database corrected, constructing a statistical perturbation model to describe degradation characteristics of the extracted features from the second database, applying the statistical perturbation model to the first database to construct a perturbed database sensitive to degradations of the biometric detection system and evaluating the biometric detection system against the first database and the perturbed database to predict a performance of the biometric detection system.
In other systems and methods, the biometric detection system preferably includes a fingerprint recognition system, and the biometric images include images of fingerprints. The step of constructing a statistical perturbation model to describe degradation characteristics of the extracted features from the second database as provided in the editing step may include estimating a difference between an ideal feature extractor and a degraded feature extractor which provided the degradation characteristics. The step of providing an edited database including a plurality of biometric images with corrected extracted features may include the steps of extracting biometric features from a database representative of a user population using a feature extractor and correcting errors committed by the feature extractor. The step of editing a second database may include the steps of extracting biometric features from a database representative of a user population using a feature extractor and correcting errors committed by the feature extractor. The step of constructing a statistical perturbation model may include the step of recording differences in the degradation characteristics of the extracted features from the second database before and after the step of editing to summarize degradation introduced by a sensor and a feature extractor of the biometric detection system.
The methods of the present invention may be implemented on a program storage device(s) readable by machine, tangibly embodying a program of instructions executable by the machine to perform the method steps for evaluating a biometric detection system.
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.